电子舌
管道(软件)
线性判别分析
预处理器
计算机科学
人工智能
机器学习
计算机硬件
模式识别(心理学)
化学
程序设计语言
食品科学
品味
作者
Giulia Magnani,Chiara Giliberti,Davide Errico,Mattia Stighezza,Simone Fortunati,Monica Mattarozzi,Andrea Boni,Valentina Bianchi,Marco Giannetto,Ilaria De Munari,Stefano Cagnoni,Maria Careri
出处
期刊:Sensors
[MDPI AG]
日期:2024-06-02
卷期号:24 (11): 3586-3586
摘要
The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.
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